Cognitive dynamic systems are inspired by the computational capability of the brain and the viewpoint that cognition is a supreme form of computation. The key idea behind this new paradigm is to mimic the human brain as well as that of other mammals with echolocation capabilities which continuously learn and react to stimulations according to four basic processes: perception-action cycle, memory, attention, and intelligence. The Impact of Cognition on Radar Technology is an essential exploration of the application of cognitive concepts in the development of modern phased array radar systems for surveillance. It starts by asking whether our current radar systems already have cognitive capabilities and then discusses topics including: mimicking the visual brain; applications to CFAR detection and receiver adaptation; cognitive radar waveform design for spectral compatibility; cognitive optimization of the transmitter-receiver pair; theory and application of cognitive control; cognition in radar target tracking; anticipative target tracking; cognition in MIMO radar, electronic warfare, and synthetic aperture radar. The book concludes with a cross-disciplinary review of cognition studies with potential lessons for radar systems.

Cognition is the set of all mental abilities and processes related to knowledge: attention, memory and working memory, judgment and evaluation, reasoning and computation, problem solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge. Cognition can also be artificial.

In this chapter, we describe a cognitive radar that mimics the visual brain [1]. Although the visual brain and radar are different in that the visual brain does not transmit a probing signal to the environment, while the active radar greatly relies on the probing signal it transmits to the environment; nevertheless, both of them are observers of the surrounding environment. As such, there is much that we can learn from the visual brain in building a new generation of cognitive radars that outperform traditional radars. In this chapter, we confine the discussion, in both analytic and experimental terms, to cognitive radar aimed at target tracking.

Cognitive dynamic systems have been inspired by the unique neural computational capability of brain and the viewpoint that cognition (in particular the human one) is a supreme form of computation. Some exemplifications within this new class of systems, which is undoubtedly among the hallmarks of the twenty-first century, are cognitive radar, control, radio, and some other engineering dynamic architectures. Haykin published two pioneering articles in the context of the cognitive radar. The key idea behind this new paradigm is to mimic the human brain as well as that of other mammals with echolocation capabilities (bats, dolphins, whales, etc.). They continuously learn and react to stimulations from the surrounding environment according to four basic processes: perception-action cycle, memory, attention, and intelligence. This last observation highlights the importance of specifying which are the “equivalents” of the aforementioned activities in a cognitive radar.

This chapter has considered the cognitive design of radar waveforms in a spectrally crowded environment where some frequency bands are shared among the radar and other telecommunication systems. Cognition provided by a REM represents the key to an intelligent and dynamic spectrum allocation. In fact, REM information induces dynamic spectral constraints on the radar waveform which is thus the result of a constrained optimization process aimed at improving some radar performance measures such as detection capabilities and ambiguity function properties. Either global and local spectral compatibility requirements have been considered at the design stage. Hence, polynomial computational complexity solution procedures have been developed to synthesize optimized radar waveforms. The performance of the synthesized signals has been analyzed studying the trade-off among the achievable SINR, spectral shape, ACF features, and radiated energy. Remarkably, the local constraint approach is able to ensure a precise control on the interference energy induced on each shared/reserved bandwidth at the price of a slight performance reduction. Possible future research tracks might concern the development of robust frameworks to contrast transmitter impurities and the cognitive fully exploitation of the available multiple dimensions, i.e., spatial, temporal, and polarizations, to improve further system performance.

In this chapter, cognition about the surrounding environment has been exploited to adapt the system to the interfering environment. Precisely, the robust joint optimization of the transmit signal and receive filter bank in the presence of signal-dependent interference has been considered. The Doppler shift of the moving target has been assumed unknown, and the worst case SINR at the output of the filter bank has been employed as figure of merit. By doing so, the effectiveness of the cognitive architecture has been investigated when the target Doppler frequency is a-priori unknown. In fact, while a rough Doppler knowledge is very reasonable during the detection confirmation or for an already tracked target, it is usually not available during the standard search radar operation and suitable cognitive algorithms are required.

Cognition is a distinctive characteristic of the human brain, which distinguishes itself from all other mammalian species. It is therefore not surprising that when we speak of cognitive control, we naturally think of cognitive control in the brain [1]. Most importantly, cognitive control resides in the executive part of the brain, reciprocally coupled to its perceptual part via the working memory [2]. The net result of this threefold combination is the perception-action cycle that embodies the environment, thereby constituting a closed-loop feedback system of a global kind.

This chapter has been devoted to the design of a tracker exploiting cognition at multiple levels. Specifically, environmental maps and characteristics of the targets, available in the dynamic database possibly learned from the feedback channel, have been used to gain improved tracking performance in a multiple targets scenario exploiting measurements provided by a tracking radar. Unlike the conventional tracking radar (which is very sensitive to false alarms and/or missed detections), the main advantage of the cognitive paradigm is the significant reduction in the number of false alarms, missed detections, false tracks, and improved true target track life. In the second part of the chapter, the focus has been on waveform selection to optimize the target tracking process. Specifically, it has been assumed that a waveform library is available at the transmitter, and the most suitable signal (in the sense of minimizing the predicted tracking estimation error) is chosen for the next dwell. The proposed algorithm is based on the use of feedback information from the receiver and exploits a standard KF. The performance of the proposed strategy has been studied in a challenging scenario accounting for a maneuvering target in the presence of thermal noise only or RF interference plus thermal noise. The results have highlighted that the adaptive feedback process guiding the waveform selection is able to provide advantages over the classic radar tracker, which does not resort to transmit adaptivity.

Anticipative target tracking encompasses a number of operational cases. Automatic artillery gun defence against shells: this may be the first need in the defence systems where anticipative tracking even of a split second is operationally relevant.Tracking of ballistic targets and prediction of impact point in the re-entry phase is quite important in defence systems. The possibility to exploit some a priori information can help the task of impact point prediction. A priori information (like maps, roads, gallery terrain orography, multi-path exploitation to mitigate blind conditions for instance in urban tracking, etc.) are very helpful in assisting the target tracking and predicting ahead in time the target trajectory. This is a form of cognitive tracking also referred to as knowledge-based tracker (KBT).

This chapter is devoted to the presentation of some miscellanea application domains which could significantly benefit by the use of cognition. The first part is devoted to the presentation of the multiple-input-multiple-output (MIMO) radar transmit beampattern design process which can highly get advantage of the cognitive paradigm. It is well known that MIMO radar is a recently emerging paradigm enabling an enhanced performance over conventional radar in terms of target detection, identification, classification, and localization [1,2]. Additionally, colocated MIMO radar allows a higher flexibility in the transmit beampattern shape [3] based on the ability to transmit distinct waveforms via the probing antennas. This last feature is particularly attractive for cognitive radar systems, where the transmitter dynamically selects the best transmit beampattern in response to the receiver feedbacks, accounting for both previous experience/measurements and stored information. For instance, if the receiver detects in some angles strong unwanted returns, due to both clutter discrete and nonthreatening targets, the transmit beampattern can be shaped to exhibit small gain values in the mentioned directions so as to suppress the interference and to avoid overloading the processor with detections of no-tactical importance. Besides, multiple target tracking can be accomplished via multiple beams in the transmit beampattern (possibly adaptively interleaved with search beams) thus enhancing the multifunctionality of the system [4].

A crossdisciplinary overview of attractive technical topics was attempted to enrich the background, which could foster new hints for the development of CR. First, the focus was on intelligence, that is deemed a key feature of CR. An old story, involving natural and artificial intelligence, which is still in its growing phase. Its evolution should be carefully monitored in view of CR exploitation. The pervasive theory of network is reviewed together with the quantitative concepts of controllability and complexity and their relation to the network topology and statistical degrees of nodes. The impact on the scheduler of modern radar and of multisensor systems is discussed. Bioinspired collective processing is widely analyzed and practical examples of radar signal and data processing are recalled. Subsequently, new discoveries and recent researches on neurosciences are reviewed and potential applications to adaptive radar signal processing and adaptive radar scheduler are hypothesized. The discovery of memristor and its practical realization with nanotechnology have been mentioned also in relation to its striking potential role as model of synapses and axon in the brain. One could argue for an adaptive radar signal processor based on memristor technology. Even though quite recently a paper has questioned the discovery of the missing memristor. Cyber-security is a global issue. It should be considered not only for conventional radar and for radar networks but for their cognitive evolution as well. Mitigation policies of this risk are briefly reviewed.